BIS-4D: Mapping soil properties and their uncertainties at 25 m resolution in the Netherlands DOI Creative Commons
Anatol Helfenstein, Vera Leatitia Mulder,

M.J.D. Hack‐ten Broeke

et al.

Published: Feb. 1, 2024

Abstract. In response to the growing societal awareness of critical role healthy soils, there is an increasing demand for accurate and high-resolution soil information inform national policies support sustainable land management decisions. Despite advancements in digital mapping initiatives like GlobalSoilMap, quantifying variability its uncertainty across space, depth, time remains a challenge. Therefore, maps key properties are often still missing on scale, which also case Netherlands. To meet this challenge fill data gap, we introduce BIS-4D, high resolution modelling platform BIS-4D delivers texture (clay, silt sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity their uncertainties at 25 m between 0–2 depth 3D space. Additionally, it provides organic matter space 1953–2023 same range. The statistical model uses machine learning informed by observations numbering 3815–855 950, depending property, 366 environmental covariates. We assess accuracy mean median predictions using design-based inference probability sample location-grouped 10-fold cross-validation, prediction interval coverage probability. found that clay, pH was highest, with efficiency coefficient (MEC) ranging 0.6–0.92 depth. Silt, matter, nitrogen (MEC = 0.27–0.78), especially phosphorus −0.11–0.38), were more difficult predict. One main limitations cannot be used quantify spatial aggregates. A step-by-step manual helps users decide whether suitable intended purpose, overview allmaps can supplementary (SI), openly available code input enhance reproducibility future updates, easily downloaded https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). fills previous gap scale GlobalSoilMap product Netherlands will hopefully facilitate inclusion as routine integral part decision systems.

Language: Английский

Remote Sensing Data for Digital Soil Mapping in French Research—A Review DOI Creative Commons
Anne C Richer-De-Forges, Qianqian Chen, Nicolas Baghdadi

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(12), P. 3070 - 3070

Published: June 12, 2023

Soils are at the crossroads of many existential issues that humanity is currently facing. a finite resource under threat, mainly due to human pressure. There an urgent need map and monitor them field, regional, global scales in order improve their management prevent degradation. This remains challenge high often complex spatial variability inherent soils. Over last four decades, major research efforts field pedometrics have led development methods allowing capture nature As result, digital soil mapping (DSM) approaches been developed for quantifying soils space time. DSM monitoring become operational thanks harmonization databases, advances modeling machine learning, increasing availability spatiotemporal covariates, including exponential increase freely available remote sensing (RS) data. The latter boosted DSM, resolution assessing changes through We present review main contributions developments French (inter)national research, which has long history both RS DSM. Thanks SPOT satellite constellation started early 1980s, communities pioneered using sensing. describes data, tools, imagery support predictions wide range properties discusses pros cons. demonstrates data frequently used (i) by considering as substitute analytical measurements, or (ii) covariates related controlling factors formation evolution. It further highlights great potential provides overview challenges prospects future sensors. opens up broad use natural monitoring.

Language: Английский

Citations

25
Recent progress on emerging technologies for trace elements-contaminated soil remediation DOI
Taoufik El Rasafi, Ayoub Haouas, Anas Tallou

et al.

Chemosphere, Journal Year: 2023, Volume and Issue: 341, P. 140121 - 140121

Published: Sept. 8, 2023

Language: Английский

Citations

24
Developing regional soil micronutrient management strategies through ensemble learning based digital soil mapping DOI Creative Commons
Shubhadip Dasgupta, Santonu Debnath, Ayan Das

et al.

Geoderma, Journal Year: 2023, Volume and Issue: 433, P. 116457 - 116457

Published: March 29, 2023

Mapping of soil micronutrient variability is critical for improving agronomic biofortification. This study used 1778 surface samples collected from four agro-climatic regions the Indo-Gangetic Plain India to produce digital maps available Zn, Cu, Fe, and Mn using 52 environmental covariates at a resolution 150 m. The prediction accuracy was compared 14 machine learning approaches their ensemble model. hybrid model outperformed all base learners subsequently producing maps. All micronutrients exhibited sufficient spatial variability. Both Zn Fe lower uncertainties. Moreover, inter-relationship between concentration in rice grain explored understand biofortification potential. linear regression models revealed moderate agreement concentrations, with R2 values 0.52–0.63 respectively. developed were predict content respective indicating potential tested approach identify specific pockets where varieties can be planted. In future, mapping herein help policymakers regional decision-making, encouraging nutrient-based subsidy investment opportunities sustainable recommendations toward micronutrient-enriched food. Further research needed develop intelligence platform DSM products resource-poor countries.

Language: Английский

Citations

23
Biochar-assisted remediation of contaminated soils under changing climate DOI
Rashida Hameed, Adeel Abbas, Ismail Khan

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 377 - 420

Published: Jan. 1, 2024

Language: Английский

Citations

14
Multiscale evaluations of global, national and regional digital soil mapping products in France DOI Creative Commons
Blandine Lemercier, Philippe Lagacherie,

Julien Amelin

et al.

Geoderma, Journal Year: 2022, Volume and Issue: 425, P. 116052 - 116052

Published: Aug. 1, 2022

As digital soil mapping (DSM) applications have been developed at multiple extents over the two last decades, large areas of world are now covered by several DSM products with similar resolution and targeted properties. Thus, from these products, end-users must carefully select one that will best meet their needs. The aim this study was to evaluate three obtained different scales (global, national regional) local territories increasing area selected in contrasting regions France (Alsace, Brittany Languedoc-Roussillon). Three topsoil (5–15 cm) properties were evaluated: clay content, pH water organic carbon content. Evaluations done both point unit supports, latter corresponding quantitative assessment visual accordance between conventional maps acknowledged quality. ability predict well increased global regional products. However, none tested able satisfactorily most (1:25,000) scale. using generally those points. also provided additional information about utility for small too few measurements perform punctual evaluation issues concerning areal-support uses These results suggest when focusing on areas, users should performance and, if unsatisfactory, invest development DSM.

Language: Английский

Citations

32
Digital soil mapping in the Russian Federation: A review DOI
Azamat Suleymanov, Dominique Arrouays, I. Yu. Savin

et al.

Geoderma Regional, Journal Year: 2024, Volume and Issue: 36, P. e00763 - e00763

Published: Jan. 18, 2024

Language: Английский

Citations

8
Geospatial evaluation of the agricultural suitability and land use compatibility in Europe's temperate continental climate region DOI Creative Commons
Andrei Dornik, Marinela Adriana Cheţan, Tania Elena Crişan

et al.

International Soil and Water Conservation Research, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Land suitability assessment is used in conjunction with geographic information systems to spatially model diverse aspects of soil functions, having the potential facilitate a sustainable increase agricultural production, reduce land degradation, or aid humans adapting climate change. Compared existing datasets, this study provides new higher resolution geospatial for several crops and uses temperate continental across Europe. To we data depicting seventeen eco-pedological indicators (e.g. texture, pH, porosity, temperature, precipitation, slope). evaluate how utilized, maps have been cross-tabulated crop map. Over entire area, wheat barley showed significant suitable southern part, potatoes, sugar beet exhibited highest extent northern parts, while corn sunflower much lower land. Water table depth, terrain slope, SOC, topsoil texture emerged as limiting factors area. Our results show that arable does not space left expansion crops, however, identified regions extensive cultivation on unsuitable more such barley, sunflower, beet, potato. It seems one action can enhance practices area better allocate each cultivated lands.

Language: Английский

Citations

7
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands DOI Creative Commons
Anatol Helfenstein, Vera Leatitia Mulder,

M.J.D. Hack‐ten Broeke

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(6), P. 2941 - 2970

Published: June 25, 2024

Abstract. In response to the growing societal awareness of critical role healthy soils, there has been an increasing demand for accurate and high-resolution soil information inform national policies support sustainable land management decisions. Despite advancements in digital mapping initiatives like GlobalSoilMap, quantifying variability its uncertainty across space, depth time remains a challenge. Therefore, maps key properties are often still missing on scale, which is also case Netherlands. To meet this challenge fill data gap, we introduce BIS-4D, modeling platform BIS-4D delivers texture (clay, silt sand content), bulk density, pH, total nitrogen, oxalate-extractable phosphorus, cation exchange capacity their uncertainties at 25 m resolution between 0 2 3D space. Additionally, it provides organic matter space 1953 2023 same range. The statistical model uses machine learning informed by observations amounting 3815 855 950, depending property, 366 environmental covariates. We assess accuracy mean median predictions using design-based inference probability sample location-grouped 10-fold cross validation (CV) prediction interval coverage probability. found that clay, pH was highest, with efficiency coefficient (MEC) ranging 0.6 0.92 depth. Silt, matter, nitrogen (MEC 0.27 0.78), especially phosphorus −0.11 0.38) were more difficult predict. One main limitations cannot be used quantify spatial aggregates. provide example good practice help users decide whether suitable intended purpose. An overview all can Supplement. Openly available code input enhance reproducibility future updates. readily downloaded https://doi.org/10.4121/0c934ac6-2e95-4422-8360-d3a802766c71 (Helfenstein et al., 2024a). fills previous gap national-scale GlobalSoilMap product Netherlands will hopefully facilitate inclusion as routine integral part decision systems.

Language: Английский

Citations

7
Cropland carbon stocks driven by soil characteristics, rainfall and elevation DOI
Fangzheng Chen, Puyu Feng, Matthew Tom Harrison

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 862, P. 160602 - 160602

Published: Dec. 6, 2022

Language: Английский

Citations

27
Soil organic matter mapping using INLA-SPDE with remote sensing based soil moisture indices and Fourier transforms decomposed variables DOI Creative Commons

Chenconghai Yang,

Lin Yang, Lei Zhang

et al.

Geoderma, Journal Year: 2023, Volume and Issue: 437, P. 116571 - 116571

Published: June 20, 2023

Generating accurate spatial information on soil organic matter (SOM) is increasingly important in the context of global environmental change. Both prediction models and covariates influence mapping results accuracy, making them factors SOM mapping. The Bayesian model INLA-SPDE an emerging model, that has shown potential digital (DSM), but its application still limited. Soil moisture, which affects water status decomposition SOM, can be a predictor for SOM. However, difficulty obtaining moisture measurements over large area using ground-based methods hinders application. Recently, high resolution remote sensing (RS) provided possible way to generate indices area. effectiveness RS-based unknown. Fourier transforms decomposed (FTD) variables based vegetation have been proven effective detecting time-series patterns crop growth, thereby improving accuracy farmland. Yet, FTD not verified other vegetation-covered areas. This paper examines use with three (NSDSIs) six compared Random Forest (RF), study diverse cover Anhui Province, China. finding indicates optimal combination covariates, yields higher than RF, increase 18% R2. Either or are When only natural best including improved by 25% terms R2, 21% LCCC, 11% RMSE. Furthermore, quantitative uncertainty maps derived INLA-SPDE. demonstrates

Language: Английский

Citations

15